Research Paper#Diffusion Models, Few-shot Learning, Dense Prediction🔬 ResearchAnalyzed: Jan 3, 2026 19:06
Learnable Diffusion Timesteps for Few-shot Dense Prediction
Published:Dec 29, 2025 05:19
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of selecting optimal diffusion timesteps in diffusion models for few-shot dense prediction tasks. It proposes two modules, Task-aware Timestep Selection (TTS) and Timestep Feature Consolidation (TFC), to adaptively choose and consolidate timestep features, improving performance in few-shot scenarios. The work focuses on universal and few-shot learning, making it relevant for practical applications.
Key Takeaways
Reference
“The paper proposes Task-aware Timestep Selection (TTS) and Timestep Feature Consolidation (TFC) modules.”